Method of bioimage data processing for revealing more meaningful anatomic features of diseased tissues

ABSTRACT

The present invention discloses a method for generating elevation maps or images of a tissue layer/boundary with respect to a fitted reference surface, comprising the steps of finding and segmenting a desired tissue layer/boundary; fitting a smooth reference surface to the segmented tissue layer/boundary; calculating elevations of the same or other tissue layer/boundary relative to the fitted reference surface; and generating maps of elevation relative to the fitted surface. The elevation can be displayed in various ways including three-dimensional surface renderings, topographical contour maps, contour maps, en-face color maps, and en-face grayscale maps. The elevation can also be combined and simultaneously displayed with another tissue layer/boundary dependent set of image data to provide additional information for diagnostics.

PRIORITY

This application is a continuation of U.S. patent application Ser. No.11/223,549, filed Sep. 9, 2005, which is incorporated herein byreference.

TECHNICAL FIELD OF THE INVENTION

One or more embodiments of the present invention relate generally tomethods for optical imaging of biological samples and for processingsuch images. In particular, the invention is a method for processing athree-dimensional image data set to generate elevation maps of tissuelayers relative to a fitted smooth surface, which can provide morediagnostic information than a pure tissue layer thickness map. Maps ofelevation may be embodied as three-dimensional surface renderings ofelevation, topographical maps, or as color or grayscale maps.

BACKGROUND OF THE INVENTION

Measurement of biological tissue surface contour or layer thickness canprovide useful diagnostic information in various applications. Forexample, arterial plaque thickness is related to the progress ofatherosclerosis, carotid vessel wall thickness is also an indicator ofcardiovascular disease risk; epidermal layer thickness is an indicatorof burn severity.

In ophthalmology, retinal thickness may be abnormally large in cases ofretinal edema or traction by membranes in the vitreous humor. On theother hand, the retina may appear thin in cases of atrophicdegeneration, chorioretinitis, or trauma to the retina. Meanwhile,changes in retinal thickness may be localized or extend over largeareas. In certain cases, the overall contour of the retina may becomeabnormal. For example, pronounced myopia, particularly due to posteriorstaphylomas, may create a highly concave retina. Detachment of theretinal pigment epithelium (RPE), subretinal cysts, or subretinal tumorsmay produce a relative convexity of the retina. Therefore, mapping theretina contour or retinal thickness makes it possible to determine theextent and severity of such conditions and to monitor progress oftreatment.

In the past, there are a number of well-established biomedical imagingtechniques that have been used for three-dimensional anatomical mappingof the eye, especially the retina, including optical coherencetomography (Zhou, Q. et al. (2004). “Mapping retinal thickness andmacular edema by high-speed three-dimensional optical coherencetomography”. Ophthalmic Technologies XIV, SPIE, 5314: 119-125),ultrasound (see for example, U.S. Pat. No. 5,293,871, U.S. Pat. No.5,562,095), and confocal microscopy (see for example, U.S. Pat. No.4,838,679; R. H. Webb (1996) “Confocal optical microscopy” Rep. Prog.Phys. 59 427-471). The three-dimensional data set has also been analyzedto identify layered structures in the tissue using a variety ofapproaches to image segmentation. (see for example, D. G. Bartsch, etal., (2004) “Optical coherence tomography: interpretation artifacts andnew algorithm”, Proc. SPIE Medical Imaging 2004: Image Processing, 5370:2140-2151; H. Ishikawa, et al., (2005) “Macular Segmentation withOptical Coherence Tomography”. Invest Ophthalmol Vis Sci.; 46:2012-201).

These prior art methods measured and/or generated a map of a tissuelayer thickness by searching for the borders of the tissue layerstructures, figuring out the inner and outer boundaries and then findingthe distance between the inner and outer boundaries. However, a majorissue associated with a tissue layer thickness map is that it sometimescannot reveal the diagnostically more meaningful features of a diseasedpart of the tissue. For example, retina thickness is defined as thevertical distance between the RPE (retinal pigment epithelium) 102 andthe ILM (inner limiting membrane) 104 as shown in FIG. 1. A sharp bump106 of the retina will often be associated with a rise in the RPE 102 aswell as the formation of a lesion 108 below the RPE 102, such that theRPE also has a broad rise. As a result, a retina thickness map such asthe color coded one shown in FIG. 2, which corresponds to FIG. 1, cannotreveal the substantially raised bump. In fact, the color coded thicknessmap shows that the thickness will only slightly increase near the bumpbut then return to normal over it. On the other hand, although atopographic map or contour of the RPE or ILM may reveal the sharp bumpbetter for this illustrated case than the retina thickness map, it wouldinclude both the sharp bump and the broader warping of the RPE boundary,making it difficult to separate the effect of the disease from theoverall shape of the RPE boundary

In light of the above, there is a need in the art for a method forgenerating elevation maps with respect to a reference fitted surface andfor using the reference surface as a means of locatingthree-dimensionally a tissue or a layer or boundary of a tissue such asthe retina, in order to provide diagnostically more meaningfulinformation about potential diseased tissue.

The present invention is a novel and non-obvious method wherein a fittedreference surface is used to create an elevation map or image of atissue layer/boundary with respect to the fitted reference surface. Useof such a fitted surface can minimize the perturbations of the surfaceassociated with disease so as to approximate the tissue surface thatwould exist if the tissue were normal. By using such a fitted surface,either of the tissue boundary being measured, or a different boundary,the effect of disease or injury is isolated from the overall shape ofthe tissue of interest, providing improved diagnostic information. Inaddition to various ways to display the elevation data relative to thefitted reference surface, the invention also combines the elevation datawith other maps or images in order to provide more meaningfulinformation for diagnostics.

SUMMARY OF THE INVENTION

One or more embodiments of the present invention satisfy one or more ofthe above-identified needs in the art. In particular, one embodiment ofthe present invention is a method for generating elevation maps orimages of a tissue layer/boundary with respect to the location of afitted reference surface, comprising the steps of finding and segmentinga desired tissue layer/boundary; fitting a smooth reference surface tothe segmented tissue layer/boundary; calculating elevations of the sameor other tissue layer/boundary relative to the fitted reference surface;and generating maps of elevation relative to the fitted surface.

One aspect of the present invention is to display the elevation invarious ways including three-dimensional surface renderings,topographical contour maps, contour maps, en-face color maps, anden-face grayscale maps.

Another aspect of the present invention is to combine and hencesimultaneously display on the same map and/or image two sets of datawith one set from the elevation relative to a fitted reference surfaceand the other set from a tissue layer/boundary dependent information,including, for example, actual thickness of a tissue layer, and imagesignal strength such as reflectance from an OCT system, birefringencefrom a polarization sensitive OCT system or a scanning laser polarimetrysystem, and intensity from a confocal imaging system.

Another aspect of the present invention is to perform the fitting toobtain the reference surface in a number of ways, including using asecond-order polynomial fit, or using Zernike or Chebyshev or otherpolynomials, or Bessel functions, or a portion of a sphere or spheroid.Additionally, the fitting can also be performed by excluding certainportions of the tissue layer/boundary, i.e. the regions of diseasedtissue, from the determination of the fitted reference surface, orfitting on more than one region of the tissue layer/boundary orsmoothing/filtering a tissue layer/boundary.

Still another aspect of the invention is to locate the general tissuelayer/boundary contour for subsequent scans, which need to follow thetissue contour closely.

Additional aspects of the invention will be set forth in part in thedescription which follows. It is to be understood that both theforegoing general description and the following detailed description areexemplary and explanatory only and are not restrictive of the invention,as claimed

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows OCT images of a retina, illustrating RPE (retinal pigmentepithelium), the ILM (inner limiting membrane), a sharp bump and alesion below the sharp bump.

FIG. 2 is a color coded retina thickness map corresponding to FIG. 1

FIG. 3 shows a flow diagram of the steps of the invented imageprocessing method.

FIG. 4 shows a retina image with the hue mapped as the square of thedistance from the fitted RPE reference surface;

FIG. 5 shows a contour plot using the color coding that represents thedistance from the ILM to a paraboloid fitted RPE reference surface;

FIG. 6 shows a pseudocolor image representing the distance from the ILMto a paraboloid fitted RPE reference surface;

FIG. 7 shows a three-dimensional rendering of the ILM surface elevationrelative to the paraboloid fitted RPE reference surface, with the colorindicating in duplicate the same elevation information.

FIG. 8 shows a three-dimensional rendering of the actual retinathickness superimposed with a pseudocolor image indicating ILM elevationwith respect to the paraboloid fitted RPE reference surface

FIG. 9 shows a three-dimensional rendering of the ILM surface elevationrelative to the paraboloid fitted RPE reference surface superimposedwith a pseudocolor image indicating actual retinal thickness;

FIG. 10 shows a pseudocolor image representing the distance from the RPEto a paraboloid fitted RPE reference surface.

FIG. 11 is a schematic diagram of a basic OCT system capable ofgenerating 3D image data that can be used in the method of the subjectinvention.

DETAILED DESCRIPTION

FIG. 3 shows one preferred embodiment the presently invented method.This method is intended to be used on image data obtained from a sample.The illustrations in this application arc based on image data derivedfrom an optical coherence tomography system (OCT) which includes bothtime domain and spectral domain OCT systems. Such an instrumentgenerates 3D intensity data corresponding to an axial reflectiondistribution arising from reflecting features in the eye. As notedabove, this information is currently used by doctors to view anddiagnosis various pathologies in the eye. A basic OCT system will bediscussed below.

Although the illustrated embodiments are limited to OCT data, the imageprocessing concepts described herein may be used with 3D image dataderived from other modalities. For example, image data may be createdusing various forms of confocal microscopy and even ultrasound imagingmodalities.

The first step in the subject method requires identification of a subsetof the image data which corresponds to a boundary surface within thesample (step 302). As used herein, boundary surface can be a limitingsurface in the sample, a surface of a layer or other interface. Theboundary should have a sufficient linear or 2D extent that it can bereasonably fitted to a geometric line or surface.

Identification of the image data corresponding to a boundary surface isperformed using a segmenting function. Methods for finding andsegmenting a desired tissue layer or boundary surface are well-known inthe art. (see for example, H. Ishikawa, et al., (2005) “MacularSegmentation with Optical Coherence Tomography”. Invest Ophthalmol VisSci.; 46: 2012-201).

Once the image data corresponding to the selected surface has beensegmented, the boundary is fitted to a substantially smooth referencesurface (step 304). There are a number of well-known methods for fittinga measured surface data points to a geometric surface. One example is asecond-order polynomial fit. Other functions, including Zernike orChebyshev polynomials, Bessel functions, or a portion of a sphere orspheroid, can also be used for surface fitting. A smooth referencesurface can be formed by fitting the shape of a tissue layer/boundarywith a function of two variables. This requires a reasonably accuratesegmentation of the chosen tissue layer/boundary and can be accomplishedusing, for example, a low-order polynomial fit in x and y.

The fitting may encompass the entire tissue layer/boundary or may beperformed on various regions of the surface, e.g., fitting the fovealpit separately from the macula, or excluding pathological regions fromthe fitting. The reference surfaces can be used to define layers in thedata that have the retinal tilt and curvature removed. In one aspect ofthe invention, these data points can be used to form en-face imagesrepresenting retinal structures in those layers. This presents anadvantage over the flat C-scan presentation of the data when imaging thecurved layers in the anatomy of the eye, since a C-scan will only showtangential slices of the retinal layers.

As noted above, use of such a fitted surface can minimize theperturbations of the surface associated with disease so as toapproximate the tissue surface that would exist if the tissue werenormal. In this case, the fitting algorithm will function to rejectpoints that are associated with the selected boundary surface but existas a result of the disease. By using such a fitted surface, either ofthe tissue boundary being measured, or a different boundary, the effectof disease or injury is isolated from the overall shape of the tissue ofinterest, providing improved diagnostic information.

In the next step of the method (306), the distances or elevationsbetween points on the reference surface and some other feature ofinterest are calculated. The feature of interest may be the actualboundary initially selected so that elevations will correspond to thedeviations between the selected surface and the associated referencesurface. The feature of interest can also be another interface orboundary within the sample.

In the next step of the subject invention (308), 2D data sets aregenerated based on the calculated distances between the referencesurface and other feature of interest. The 2D data sets can be used tocreate elevation maps. The elevation maps may be created based onpseudocolor or gray scales with elevation encoded as color as shown inFIG. 6 or intensity (not shown); or as topographical contour maps withelevation encoded as contour height as shown in FIG. 5 or as threedimensionally rendered topographical maps. These types of maps may alsobe combined to simultaneously display on the same map two sets of data,one for elevation relative to a fitted reference surface and the otherfor either another elevation relative to another reference surface, oran actual tissue layer thickness, or the originally collected imagesignal strength such as OCT or confocal optical signal strength for atissue layer or other processed/unprocessed tissue layer/boundary datasuch as birefringence measured from a polarization sensitive OCT systemor a scanning laser polarimetry system. For example, the distance fromILM relative to the RPE (i.e. the actual retina thickness) could bedisplayed as a contour map superimposed on a pseudocolor map of ILMelevation relative to a fitted RPE reference surface (not shown).Similarly, a pseudocolor map may be applied to a three-dimensionalsurface rendering in order to simultaneously display multipleinformation on elevation, thickness, reflectance or others.

In addition to the fact that by analyzing the curvature of the fittedreference surface, abnormal tissue layer curvatures (for example, theretina curvature for the case of pathologic myopia) can be diagnosed,the present invention has a number of other advantages over prior artmethods as it can provide additional useful information for diagnosingdiseased tissues. For example, the fitted reference surface can be usedas a basis for elevation maps of retinal layers, to diagnose abnormalcurvature of the retina, or as a guide for subsequent contour-followingscans of that eye. Using such a fitted reference surface as a basis for“thickness” measurements could give more robust results because theexact topography of a deteriorating RPE may be more difficult todetermine than the general shape of that layer. The reference surfacedetermined by fitting will be more consistent than that determined byfollowing the RPE in detail, especially in diseased eyes that may havebreaks or complex variations in the RPE. As an example, map(s) ofelevation can be displayed in the form of topographical contour map(s)applied to surface renderings of elevation. FIG. 5 shows a color contourcoding that represents the distance from the ILM to a paraboloidreference surface fitted to the RPE. The corrected effective retinal“thickness” relative to the fitted surface is shown in microns on thecolor bar to the right of the map.

Additionally, presentation of topographic information relative to afitted reference surface or surfaces can generate images with addedinformation, for example:

-   -   (1) a 2-D false color image giving an en-face presentation of        distance from the ILM to a reference surface fitted to the RPE        can provide information on the effective retinal thickness which        does not include thickness variations caused by small        perturbations in the RPE. FIG. 6 shows a pseudocolor image        representing the distance from the ILM to a paraboloid fitted        RPE reference surface;    -   (2) a 2-D false color image giving an en-face presentation of        distance from the actual RPE to a reference surface fitted to        the RPE itself can highlight localized variations in the RPE        which may be associated with disease. FIG. 10 shows a        pseudocolor image representing the distance from the RPE to a        paraboloid fitted RPE reference surface.    -   (3) a color mapping for 2-D images or translucent 3-D        renderings, in which brightness represents reflectance and hue        represents distance from the reference surface, can provide an        illustration of the height of brightly reflecting layers from        the reference surface without detailed segmentation of all the        layers. For cases of complicated pathology, fully automated        segmentation algorithms for multiple layers may be too        time-consuming for routine use and manual user intervention may        not be practical. For example, in the case of an RPE detachment,        the layers that are normally brightly reflecting very near to        the reference surface are now very far removed from the        reference surface. The human eye is very sensitive to hue        changes, and a convenient way to illustrate such RPE detachment        is to use hue changes. FIG. 4 shows a 2-D retina image with the        hue mapped as the square of the distance from the fitted RPE        reference surface.    -   (4) a flattened 3-D rendering of a retinal surface or surfaces,        which shows the elevation relative to a reference surface rather        than its actual contour elevation in the image data can provide        a more meaningful view of the retina anatomic features. FIG. 7        shows a three-dimensional rendering of the ILM elevation        relative to a paraboloid fitted RPE reference surface. Different        pupil positions cause tilt in the recorded retinal images, and        variations in working distance cause different curvature in the        recorded retinal images. Warping the OCT data to flatten this        image may have advantages for more standardized presentation,        regardless of exact pupil and z position, aiding comparisons of        the images between visits, or registration of multiple scans for        other purposes such as speckle reduction.    -   (5) a 3-D rendering of the ILM with a color mapping for        elevation relative to the reference surface fitted to the RPE        can combine the false color image previously described in (1),        along with the actual retina thickness which could indicate the        presence of traction by membranes on its surface. FIG. 8 shows a        three-dimensional rendering of the actual retina thickness        superimposed with a pseudocolor image indicating ILM elevation        with respect to the paraboloid fitted RPE reference surface. On        the other hand, such a superimposed 3D rendering can also be the        other way round. For example, FIG. 9 shows a three-dimensional        rendering of the ILM surface elevation relative to the        paraboloid fitted RPE reference surface superimposed with a        pseudocolor image indicating actual retinal thickness. The ILM        elevation may reflect the position in the image data or a        rendering that is flattened to a reference surface as previously        described in (4).

Axial resolution may be wasted if the z-range of the scan does notfollow the contour of the retinal tissue. A few initial scans could beused to determine the reference surface, then a retina-following scancould be performed by changing the OCT reference arm length to followthe predetermined reference surface as the transverse scans areperformed.

Note that the present invention can also be applied to B-scan images inwhich case, the term reference surface should interpreted as a referencecurved line and tissue layer/boundary will also be interpreted as acurved line. FIG. 4 shows a B-scan retina image with the hue mapped asthe square of the distance from the fitted RPE reference surface. Thevertical distance in the image relative to the location of the fittedRPE reference surface is encoded as a color which is used to highlightthe image.

The present invention does not need to follow the exact sequence asshown in FIG. 3, as other additional steps can be inserted to performsubstantially equivalent operations. For example, the fitting operationmay be approximated by smoothing or otherwise filtering the retinallayer. Also, the reference surface might not be the direct result offitting, but some filtered version thereof. Furthermore, a variation ofthis idea could use two such reference surfaces, rather than theelevation from an unfitted surface to a reference surface.

The presently invented method could be applied to the analysis of theretina or curvature of the eye in existing and future OCT systems. Itcan also be used for analysis of other biological tissues such as theskin. Also, it may find use in ultrasound and confocal microscopysystems as well.

FIG. 11 shows a basic spectrometer based spectral domain OCT system1100. The light wave from the broadband emitter 1110 is preferablycoupled through a short length of an optical fiber 1112 to an input port(port I) of a fiber optic coupler 1114, which splits the incoming lightbeam into the two arms of a Michelson interferometer. The two arms eachhave a section of optical fiber (1116 and 1118) that guides the splitlight beam from the two output ports (port II and port III) of the fibercoupler 1114 to a sample 1124 and a reference reflector 1126respectively. For both the sample arm and the reference arm, at theterminating portion of each fiber, there may be a module containingoptical elements to collimate or focus or scan the beam. Illustrated inFIG. 11 as an embodiment are two focusing lenses 1120 and 1122. Thereturned light waves from the sample 1124 and the reference reflector1126 are directed back through the same optical path of the sample andreference arms and are combined in fiber coupler 1114. A portion of thecombined light beam is directed through a section of optical fiber 1130from port IV of the fiber coupler 1114 to a spectrometer 1150. Insidethe spectrometer, the light beam is dispersed by a grating 1152 andfocused onto a detector array 1154. Note that the principle of operationof a tunable laser based swept source OCT is very similar to that of aspectrometer based spectral domain OCT system (see for example, Choma,M. A. et al. (2003). “Sensitivity advantage of swept source and Fourierdomain optical coherence tomography.” Optics Express 11(18): 2183-2189),hence the spectral domain OCT system for obtaining the 3D image data setcan also be a swept source OCT system.

Although various embodiments that incorporate the teachings of thepresent invention have been shown and described in detail herein, thoseskilled in the art can readily devise many other varied embodiments thatstill incorporate these teachings.

The following references are hereby incorporated by reference:

US Patent Documents

U.S. Pat. No. 4,838,679

U.S. Pat. No. 5,293,871

U.S. Pat. No. 5,562,095

U.S. provisional patent application, Ser. No. 60/632,387

Other Publications

Adaikkappan, M. et al., (2002) “Evaluation of Carotid Atherosclerosis byB-Mode Ultrasonographic Study in Hypertensive Patients Compared withNormotensive Patients” Ind J Radiol Imag; 12:3:365-368.

Bartsch, D. G. et al., (2004) “Optical coherence tomography:interpretation artifacts and new algorithm”, Proc. SPIE Medical Imaging2004: Image Processing, 5370: 2140-2151.

Choma, M. A. et al. (2003). “Sensitivity advantage of swept source andFourier domain optical coherence tomography.” Optics Express 11(18):2183-2189

Ishikawa, H. et al., (2005) “Macular Segmentation with Optical CoherenceTomography”. Invest Ophthalmol Vis Sci.; 46: 2012-201.

Webb, R. H. (1996) “Confocal optical microscopy” Rep. Prog. Phys. 59427-471.

Zhou, Q. et al. (2004). “Mapping retinal thickness and macular edema byhigh-speed three-dimensional optical coherence tomography”. OphthalmicTechnologies XIV, SPIE, 5314: 119-125.

1. (canceled)
 2. A method for generating image maps from a 3D image dataset, said data set obtained from imaging an eye using an imaging device,said eye having at least one boundary surface associated therewith, saidmethod comprising the steps of: identifying a boundary surface withinthe 3D image data set; generating a substantially smooth referencesurface from the boundary surface; identifying a second surface withinthe 3D image data set; calculating the distance between points on thesmooth reference surface and corresponding points on the second surface;generating an image map of the calculated distances; and displaying theimage map.
 3. A method as recited in claim 2, wherein the boundarysurface is the RPE.
 4. A method as recited in claim 2, wherein theboundary surface is the front surface of the cornea.
 5. A method asrecited in claim 2, wherein the boundary surface is the ILM.
 6. A methodas recited in claim 2, wherein the substantially smooth referencesurface is generated by fitting the boundary surface.
 7. A method asrecited in claim 2, wherein the substantially smooth reference surfaceis generated by smoothing the boundary surface.
 8. A method as recitedin claim 2, wherein the substantially smooth reference surface isgenerated by filtering the boundary surface.
 9. A method for generatingen-face images representing retinal structures in the eye from a 3Dimage data set, said data set obtained from imaging an eye using animaging device, said eye having at least one boundary surface associatedtherewith, said method comprising the steps of: identifying a boundarysurface within the 3D image data set; generating a substantially smoothreference surface from the boundary surface; defining a layer in the 3Ddata set using the smooth reference surface in order to remove retinaltilt and curvature from said layer; generating an en-face image usingdata points within the defined layer; and displaying the en-face image.10. A method as recited in claim 9, wherein the boundary surface is theRPE.
 11. A method as recited in claim 9, wherein the boundary surface isthe front surface of the cornea.
 12. A method as recited in claim 9,wherein the boundary surface is the ILM.
 13. A method as recited inclaim 9, wherein the substantially smooth reference surface is generatedby fitting the boundary surface.
 14. A method as recited in claim 9,wherein the substantially smooth reference surface is generated bysmoothing the boundary surface.
 15. A method as recited in claim 9,wherein the substantially smooth reference surface is generated byfiltering the boundary surface.
 16. A method as recited in claim 9,wherein the substantially smooth reference surface is generated using atleast one selected region of the boundary surface.
 17. A method asrecited in claim 9, wherein the substantially smooth reference surfaceis generated by excluding at least one region of the boundary surface.18. A method as recited in claim 9, wherein the layer in the 3D data setis defined using a second surface in addition to the smooth referencesurface.
 19. A method for generating image maps from a 3D image dataset, said data set obtained from imaging an eye using an imaging device,said eye having at least one boundary surface associated therewith, saidmethod comprising the steps of: identifying a boundary surface withinthe 3D image data set; selecting at least one region of the boundarysurface; generating a substantially smooth reference surface to the 3Dimage data using the at least one selected region of the boundarysurface; identifying a second surface within the 3D image data set;calculating the distance between points on the smooth reference surfaceand corresponding points on the second surface; generating an image mapof the calculated distances; and displaying the image map.
 20. A methodas recited in claim 19, wherein the substantially smooth referencesurface is generated by fitting the boundary surface.
 21. A method asrecited in claim 19, wherein the substantially smooth reference surfaceis generated by smoothing the boundary surface.
 22. A method as recitedin claim 19, wherein the substantially smooth reference surface isgenerated by filtering the boundary surface.
 23. A method as recited inclaim 19, further comprising displaying the difference between thesmooth reference surface and the boundary surface.
 24. A method forgenerating image maps from a 3D image data set, said data set obtainedfrom imaging an eye using an imaging device, said eye having at leastone boundary surface associated therewith, said method comprising thesteps of: identifying a boundary surface within the 3D image data set;excluding at least one region of the boundary surface; generating asubstantially smooth reference surface to the 3D image data over theremaining regions of the boundary surface; identifying a second surfacewithin the 3D image data set; calculating the distance between points onthe smooth reference surface and corresponding points on the secondsurface; generating an image map of the calculated distances; anddisplaying the image map.
 25. A method as recited in claim 24, whereinthe substantially smooth reference surface is generated by fitting theboundary surface.
 26. A method as recited in claim 24, wherein thesubstantially smooth reference surface is generated by smoothing theboundary surface.
 27. A method as recited in claim 24, wherein thesubstantially smooth reference surface is generated by filtering theboundary surface.
 28. A method as recited in claim 24, wherein the atleast one region of the boundary surface is excluded based on aperturbation of the tissue of the eye in that region.
 29. A method asrecited in claim 28, wherein the perturbation is an abnormality of thetissue.
 30. A method as recited in claim 28, wherein the perturbation isa local variation of the tissue.
 31. A method as recited in claim 28,wherein the perturbation is an abrupt change in the tissue direction.32. A method for generating image maps from a 3D image data set, saiddata set obtained from imaging an eye using an imaging device, said eyehaving at least one boundary surface associated therewith, said methodcomprising the steps of: identifying a boundary surface located withinthe retina in the 3D image data set; generating a substantially smoothreference surface from the boundary surface; identifying a secondsurface within the retina in the 3D image data set; calculating thedistance between points on the smooth reference surface andcorresponding points on the second surface; generating an image map ofthe calculated distances; and displaying the image map.
 33. A method asrecited in claim 32, wherein the substantially smooth reference surfaceis generated by fitting the boundary surface.
 34. A method as recited inclaim 32, wherein the substantially smooth reference surface isgenerated by smoothing the boundary surface.
 35. A method as recited inclaim 32, wherein the substantially smooth reference surface isgenerated by filtering the boundary surface.